The document provides an introduction to artificial intelligence and machine learning, explaining what machine learning is, the different types including supervised, unsupervised and reinforcement learning, and when and how machine learning can be used including in applications like speech recognition, natural language processing, and using big data and high performance computing. It also gives examples of how machine learning is used at Empirix in applications like speech recognition, improving sales and marketing, and developing a support chatbot.

1. Intro to Artificial
Intelligence
by Govind Mudumbai

2. Index
 Why this Topic?
 What is Machine Learning?
 Types of ML and their applications
 Why now?
 When and how to use? Process
 Applications at Empirix

3. Why this topic
 Machine Learning – subfield of AI
 Deep Learning – Neural Networks
 Reinforcement Learning
 Blockchain – Secure, Immutable, Distributed Ledger
 Gene Cell Therapy & CRISPR – Gene Editing
 Quantum Computing – Super fast. Qubits
Feynman Technique

4. What is Machine Learning?
 Algorithm to predict with probability by recognizing a pattern
 Think of it like a baby/kid learning stuff as they grow
 Process:
 Predict – Hypothesis & Parameters
 Run Error function – Cost Function
 Learn – Minimize Cost Function

5. Types of ML
Unsupervised Learning
Reinforcement Learning
Supervised Learning

6. Unsupervised Learning
 Data is not labeled
 Take data and puts in a bin according to some its properties
 Segments data by population
 Clustering
 Examples:
 Google News articles
 Group people according to Genes/Genome
 Astronomical Data Analysis
 Market Segmentation

7. Reinforcement Learning
 Kind of true AI?!
 Semi-supervised learning
 Not fully labeled dataset
 Rewards/Goal based learning
 Algorithm figures out on its own the rules & best strategy to
achieve goal
 Deep Reinforcement Learning – Deep Q Networks
 Pretty advanced – very promising
 Examples:
 Self driving Car
 AlphaGo, Poker, Chess, Super-Mario, Atari and other games
 Natural Language Processing
 Robots taking a step

8. Supervised Learning
 Training data is labeled
 Algorithm is trained
 Linear Regression – output a number
 What temperature tomorrow?
 Price of house
 How many units will sell
 Logistic Regression
 Binary Classification: spam or not, cancer or not, will customer buy or not etc.
 Multiclass Classification: genre of movie, product category in Craigslist etc.
 Deep Learning/Neural Networks
 Emulate the way our brain works
 Multiple layers of neurons: thus ‘deep’

9. Applications of Supervised Learning in Industry
 Speech Recognition
 Natural Language Processing – Siri & Google Assistant
 Image Processing
 Recognizing Faces, OCR etc.
 Health diagnostics – Radiology
 Chatbot
 Judicial Decisions – circumvent human bias
 Online Advertisement
 Spam filtering
 Sentiment Analysis
 And many many more……

10. Why now?
High Performance Computing – HPC
Big Data

11. HPC
 Matrix-multiplication operations – need lot of resources
 GPU based computing - NVidia
 Cloud Computing – AWS, Azure, Google Cloud etc.
 GPU-based VM Instances for running training algorithms
 Services provided via API

12. Big Data
 Amount of data generated recently
 Large companies like Facebook, Google, Amazon, Baidu
 China

13. When to Use Machine Learning
 You cannot code the rules: Many human tasks (such as recognizing
whether an email is spam or not spam) cannot be adequately solved
using a simple (deterministic), rule-based solution. A large number of
factors could influence the answer. When rules depend on too many
factors and many of these rules overlap or need to be tuned very finely,
it soon becomes difficult for a human to accurately code the rules. You
can use ML to effectively solve this problem.
 You cannot scale: You might be able to manually recognize a few
hundred emails and decide whether they are spam or not. However,
this task becomes tedious for millions of emails. ML solutions are
effective at handling large-scale problems.
http://docs.aws.amazon.com/machine-learning/latest/dg/when-to-use-machine-learning.html

14. Data Science Process
 Define the problem
 Collect data for the problem
 Clean data
 Data Wrangling
 Dividing data into training and test sets
 Feature Engineering
 Run ML Algorithms
 Use resulting features
 Generate Predictions
 Visualize Results – Graphs, Charts
 Present Results

15. Math
 Linear Algebra
 Statistics & Probability
 Calculus
 Languages Used: Python, R, Matlab/Octave
 ML Frameworks: TensorFlow, Theano, Keras, PyTorch
 Visualization: D3js, Tableau
 Data Wrangling/Storage: All big data techs
 Apache Spark, Redis
 Apache Hadoop – HDFS, Hive, Pig, HBase
Technical Trends

16. At Empirix
 Speech Recognition – instead of Nuance SR
 Improve Sales/Marketing – using data in Salesforce
 A support chatbot
 Pattern recognition from all the customer data – New
opportunities
Tell me more…